Overview

Dataset statistics

Number of variables19
Number of observations36275
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 MiB
Average record size in memory152.0 B

Variable types

Categorical9
Numeric10

Dataset

DescriptionCan you predict if customer is going to cancel the reservation ?
URL
Copyright(c) Mr. Eslam Fouad 2023

Alerts

Booking_ID has a high cardinality: 36275 distinct valuesHigh cardinality
no_of_previous_bookings_not_canceled is highly overall correlated with repeated_guestHigh correlation
repeated_guest is highly overall correlated with no_of_previous_bookings_not_canceledHigh correlation
no_of_adults is highly imbalanced (52.4%)Imbalance
required_car_parking_space is highly imbalanced (80.1%)Imbalance
room_type_reserved is highly imbalanced (62.5%)Imbalance
repeated_guest is highly imbalanced (82.8%)Imbalance
no_of_previous_cancellations is highly skewed (γ1 = 25.19987595)Skewed
Booking_ID is uniformly distributedUniform
Booking_ID has unique valuesUnique
no_of_children has 33577 (92.6%) zerosZeros
no_of_weekend_nights has 16872 (46.5%) zerosZeros
no_of_week_nights has 2387 (6.6%) zerosZeros
lead_time has 1297 (3.6%) zerosZeros
no_of_previous_cancellations has 35937 (99.1%) zerosZeros
no_of_previous_bookings_not_canceled has 35463 (97.8%) zerosZeros
avg_price_per_room has 545 (1.5%) zerosZeros
no_of_special_requests has 19777 (54.5%) zerosZeros

Reproduction

Analysis started2023-06-13 14:37:26.082675
Analysis finished2023-06-13 14:38:10.704385
Duration44.62 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Booking_ID
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct36275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size283.5 KiB
INN00001
 
1
INN24187
 
1
INN24181
 
1
INN24182
 
1
INN24183
 
1
Other values (36270)
36270 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters290200
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36275 ?
Unique (%)100.0%

Sample

1st rowINN00001
2nd rowINN00002
3rd rowINN00003
4th rowINN00004
5th rowINN00005

Common Values

ValueCountFrequency (%)
INN00001 1
 
< 0.1%
INN24187 1
 
< 0.1%
INN24181 1
 
< 0.1%
INN24182 1
 
< 0.1%
INN24183 1
 
< 0.1%
INN24184 1
 
< 0.1%
INN24185 1
 
< 0.1%
INN24186 1
 
< 0.1%
INN24188 1
 
< 0.1%
INN24179 1
 
< 0.1%
Other values (36265) 36265
> 99.9%

Length

2023-06-13T14:38:10.924594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inn00001 1
 
< 0.1%
inn00007 1
 
< 0.1%
inn00070 1
 
< 0.1%
inn00009 1
 
< 0.1%
inn00003 1
 
< 0.1%
inn00004 1
 
< 0.1%
inn00005 1
 
< 0.1%
inn00006 1
 
< 0.1%
inn00008 1
 
< 0.1%
inn00020 1
 
< 0.1%
Other values (36265) 36265
> 99.9%

Most occurring characters

ValueCountFrequency (%)
N 72550
25.0%
I 36275
12.5%
1 24958
 
8.6%
0 24953
 
8.6%
2 24934
 
8.6%
3 21134
 
7.3%
4 14858
 
5.1%
5 14858
 
5.1%
6 14133
 
4.9%
7 13853
 
4.8%
Other values (2) 27694
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 181375
62.5%
Uppercase Letter 108825
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 24958
13.8%
0 24953
13.8%
2 24934
13.7%
3 21134
11.7%
4 14858
8.2%
5 14858
8.2%
6 14133
7.8%
7 13853
7.6%
8 13847
7.6%
9 13847
7.6%
Uppercase Letter
ValueCountFrequency (%)
N 72550
66.7%
I 36275
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 181375
62.5%
Latin 108825
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 24958
13.8%
0 24953
13.8%
2 24934
13.7%
3 21134
11.7%
4 14858
8.2%
5 14858
8.2%
6 14133
7.8%
7 13853
7.6%
8 13847
7.6%
9 13847
7.6%
Latin
ValueCountFrequency (%)
N 72550
66.7%
I 36275
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 290200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 72550
25.0%
I 36275
12.5%
1 24958
 
8.6%
0 24953
 
8.6%
2 24934
 
8.6%
3 21134
 
7.3%
4 14858
 
5.1%
5 14858
 
5.1%
6 14133
 
4.9%
7 13853
 
4.8%
Other values (2) 27694
 
9.5%

no_of_adults
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.5 KiB
2
26108 
1
7695 
3
 
2317
0
 
139
4
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36275
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 26108
72.0%
1 7695
 
21.2%
3 2317
 
6.4%
0 139
 
0.4%
4 16
 
< 0.1%

Length

2023-06-13T14:38:11.312589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T14:38:11.737609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 26108
72.0%
1 7695
 
21.2%
3 2317
 
6.4%
0 139
 
0.4%
4 16
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 26108
72.0%
1 7695
 
21.2%
3 2317
 
6.4%
0 139
 
0.4%
4 16
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36275
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 26108
72.0%
1 7695
 
21.2%
3 2317
 
6.4%
0 139
 
0.4%
4 16
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 36275
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 26108
72.0%
1 7695
 
21.2%
3 2317
 
6.4%
0 139
 
0.4%
4 16
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36275
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 26108
72.0%
1 7695
 
21.2%
3 2317
 
6.4%
0 139
 
0.4%
4 16
 
< 0.1%

no_of_children
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10527912
Minimum0
Maximum10
Zeros33577
Zeros (%)92.6%
Negative0
Negative (%)0.0%
Memory size283.5 KiB
2023-06-13T14:38:12.087426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.40264806
Coefficient of variation (CV)3.8245767
Kurtosis36.981856
Mean0.10527912
Median Absolute Deviation (MAD)0
Skewness4.7103495
Sum3819
Variance0.16212546
MonotonicityNot monotonic
2023-06-13T14:38:12.442667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 33577
92.6%
1 1618
 
4.5%
2 1058
 
2.9%
3 19
 
0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
0 33577
92.6%
1 1618
 
4.5%
2 1058
 
2.9%
3 19
 
0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 2
 
< 0.1%
3 19
 
0.1%
2 1058
 
2.9%
1 1618
 
4.5%
0 33577
92.6%

no_of_weekend_nights
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81072364
Minimum0
Maximum7
Zeros16872
Zeros (%)46.5%
Negative0
Negative (%)0.0%
Memory size283.5 KiB
2023-06-13T14:38:12.784261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.87064361
Coefficient of variation (CV)1.0739092
Kurtosis0.29885756
Mean0.81072364
Median Absolute Deviation (MAD)1
Skewness0.73761596
Sum29409
Variance0.7580203
MonotonicityNot monotonic
2023-06-13T14:38:13.128382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 16872
46.5%
1 9995
27.6%
2 9071
25.0%
3 153
 
0.4%
4 129
 
0.4%
5 34
 
0.1%
6 20
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 16872
46.5%
1 9995
27.6%
2 9071
25.0%
3 153
 
0.4%
4 129
 
0.4%
5 34
 
0.1%
6 20
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 20
 
0.1%
5 34
 
0.1%
4 129
 
0.4%
3 153
 
0.4%
2 9071
25.0%
1 9995
27.6%
0 16872
46.5%

no_of_week_nights
Real number (ℝ)

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2043005
Minimum0
Maximum17
Zeros2387
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size283.5 KiB
2023-06-13T14:38:13.507841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum17
Range17
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4109049
Coefficient of variation (CV)0.6400692
Kurtosis7.7982839
Mean2.2043005
Median Absolute Deviation (MAD)1
Skewness1.5993504
Sum79961
Variance1.9906525
MonotonicityNot monotonic
2023-06-13T14:38:13.866883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 11444
31.5%
1 9488
26.2%
3 7839
21.6%
4 2990
 
8.2%
0 2387
 
6.6%
5 1614
 
4.4%
6 189
 
0.5%
7 113
 
0.3%
10 62
 
0.2%
8 62
 
0.2%
Other values (8) 87
 
0.2%
ValueCountFrequency (%)
0 2387
 
6.6%
1 9488
26.2%
2 11444
31.5%
3 7839
21.6%
4 2990
 
8.2%
5 1614
 
4.4%
6 189
 
0.5%
7 113
 
0.3%
8 62
 
0.2%
9 34
 
0.1%
ValueCountFrequency (%)
17 3
 
< 0.1%
16 2
 
< 0.1%
15 10
 
< 0.1%
14 7
 
< 0.1%
13 5
 
< 0.1%
12 9
 
< 0.1%
11 17
 
< 0.1%
10 62
0.2%
9 34
0.1%
8 62
0.2%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.5 KiB
Meal Plan 1
27835 
Not Selected
5130 
Meal Plan 2
3305 
Meal Plan 3
 
5

Length

Max length12
Median length11
Mean length11.14142
Min length11

Characters and Unicode

Total characters404155
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMeal Plan 1
2nd rowNot Selected
3rd rowMeal Plan 1
4th rowMeal Plan 1
5th rowNot Selected

Common Values

ValueCountFrequency (%)
Meal Plan 1 27835
76.7%
Not Selected 5130
 
14.1%
Meal Plan 2 3305
 
9.1%
Meal Plan 3 5
 
< 0.1%

Length

2023-06-13T14:38:14.285342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T14:38:14.704256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
meal 31145
30.0%
plan 31145
30.0%
1 27835
26.8%
not 5130
 
4.9%
selected 5130
 
4.9%
2 3305
 
3.2%
3 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
l 67420
16.7%
67420
16.7%
a 62290
15.4%
e 46535
11.5%
M 31145
7.7%
P 31145
7.7%
n 31145
7.7%
1 27835
6.9%
t 10260
 
2.5%
N 5130
 
1.3%
Other values (6) 23830
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 233040
57.7%
Uppercase Letter 72550
 
18.0%
Space Separator 67420
 
16.7%
Decimal Number 31145
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 67420
28.9%
a 62290
26.7%
e 46535
20.0%
n 31145
13.4%
t 10260
 
4.4%
o 5130
 
2.2%
c 5130
 
2.2%
d 5130
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
M 31145
42.9%
P 31145
42.9%
N 5130
 
7.1%
S 5130
 
7.1%
Decimal Number
ValueCountFrequency (%)
1 27835
89.4%
2 3305
 
10.6%
3 5
 
< 0.1%
Space Separator
ValueCountFrequency (%)
67420
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 305590
75.6%
Common 98565
 
24.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 67420
22.1%
a 62290
20.4%
e 46535
15.2%
M 31145
10.2%
P 31145
10.2%
n 31145
10.2%
t 10260
 
3.4%
N 5130
 
1.7%
o 5130
 
1.7%
S 5130
 
1.7%
Other values (2) 10260
 
3.4%
Common
ValueCountFrequency (%)
67420
68.4%
1 27835
28.2%
2 3305
 
3.4%
3 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 404155
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 67420
16.7%
67420
16.7%
a 62290
15.4%
e 46535
11.5%
M 31145
7.7%
P 31145
7.7%
n 31145
7.7%
1 27835
6.9%
t 10260
 
2.5%
N 5130
 
1.3%
Other values (6) 23830
 
5.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.5 KiB
0
35151 
1
 
1124

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36275
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 35151
96.9%
1 1124
 
3.1%

Length

2023-06-13T14:38:15.091028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T14:38:15.486884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 35151
96.9%
1 1124
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 35151
96.9%
1 1124
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36275
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35151
96.9%
1 1124
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 36275
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 35151
96.9%
1 1124
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36275
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35151
96.9%
1 1124
 
3.1%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.5 KiB
Room_Type 1
28130 
Room_Type 4
6057 
Room_Type 6
 
966
Room_Type 2
 
692
Room_Type 5
 
265
Other values (2)
 
165

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters399025
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoom_Type 1
2nd rowRoom_Type 1
3rd rowRoom_Type 1
4th rowRoom_Type 1
5th rowRoom_Type 1

Common Values

ValueCountFrequency (%)
Room_Type 1 28130
77.5%
Room_Type 4 6057
 
16.7%
Room_Type 6 966
 
2.7%
Room_Type 2 692
 
1.9%
Room_Type 5 265
 
0.7%
Room_Type 7 158
 
0.4%
Room_Type 3 7
 
< 0.1%

Length

2023-06-13T14:38:15.832800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T14:38:16.251850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
room_type 36275
50.0%
1 28130
38.8%
4 6057
 
8.3%
6 966
 
1.3%
2 692
 
1.0%
5 265
 
0.4%
7 158
 
0.2%
3 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 72550
18.2%
R 36275
9.1%
m 36275
9.1%
_ 36275
9.1%
T 36275
9.1%
y 36275
9.1%
p 36275
9.1%
e 36275
9.1%
36275
9.1%
1 28130
 
7.0%
Other values (6) 8145
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 217650
54.5%
Uppercase Letter 72550
 
18.2%
Connector Punctuation 36275
 
9.1%
Space Separator 36275
 
9.1%
Decimal Number 36275
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28130
77.5%
4 6057
 
16.7%
6 966
 
2.7%
2 692
 
1.9%
5 265
 
0.7%
7 158
 
0.4%
3 7
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
o 72550
33.3%
m 36275
16.7%
y 36275
16.7%
p 36275
16.7%
e 36275
16.7%
Uppercase Letter
ValueCountFrequency (%)
R 36275
50.0%
T 36275
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 36275
100.0%
Space Separator
ValueCountFrequency (%)
36275
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 290200
72.7%
Common 108825
 
27.3%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 36275
33.3%
36275
33.3%
1 28130
25.8%
4 6057
 
5.6%
6 966
 
0.9%
2 692
 
0.6%
5 265
 
0.2%
7 158
 
0.1%
3 7
 
< 0.1%
Latin
ValueCountFrequency (%)
o 72550
25.0%
R 36275
12.5%
m 36275
12.5%
T 36275
12.5%
y 36275
12.5%
p 36275
12.5%
e 36275
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 399025
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 72550
18.2%
R 36275
9.1%
m 36275
9.1%
_ 36275
9.1%
T 36275
9.1%
y 36275
9.1%
p 36275
9.1%
e 36275
9.1%
36275
9.1%
1 28130
 
7.0%
Other values (6) 8145
 
2.0%

lead_time
Real number (ℝ)

Distinct352
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.232557
Minimum0
Maximum443
Zeros1297
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size283.5 KiB
2023-06-13T14:38:16.682089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q117
median57
Q3126
95-th percentile273
Maximum443
Range443
Interquartile range (IQR)109

Descriptive statistics

Standard deviation85.930817
Coefficient of variation (CV)1.0081924
Kurtosis1.1795941
Mean85.232557
Median Absolute Deviation (MAD)47
Skewness1.2924915
Sum3091811
Variance7384.1053
MonotonicityNot monotonic
2023-06-13T14:38:17.120321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1297
 
3.6%
1 1078
 
3.0%
2 643
 
1.8%
3 630
 
1.7%
4 628
 
1.7%
5 577
 
1.6%
6 519
 
1.4%
8 436
 
1.2%
7 429
 
1.2%
12 412
 
1.1%
Other values (342) 29626
81.7%
ValueCountFrequency (%)
0 1297
3.6%
1 1078
3.0%
2 643
1.8%
3 630
1.7%
4 628
1.7%
5 577
1.6%
6 519
1.4%
7 429
 
1.2%
8 436
 
1.2%
9 332
 
0.9%
ValueCountFrequency (%)
443 22
 
0.1%
433 20
 
0.1%
418 60
0.2%
386 69
0.2%
381 2
 
< 0.1%
377 69
0.2%
372 1
 
< 0.1%
361 5
 
< 0.1%
359 16
 
< 0.1%
355 1
 
< 0.1%

arrival_year
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.5 KiB
2018
29761 
2017
6514 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters145100
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 29761
82.0%
2017 6514
 
18.0%

Length

2023-06-13T14:38:17.542135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T14:38:17.939562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2018 29761
82.0%
2017 6514
 
18.0%

Most occurring characters

ValueCountFrequency (%)
2 36275
25.0%
0 36275
25.0%
1 36275
25.0%
8 29761
20.5%
7 6514
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145100
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 36275
25.0%
0 36275
25.0%
1 36275
25.0%
8 29761
20.5%
7 6514
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 145100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 36275
25.0%
0 36275
25.0%
1 36275
25.0%
8 29761
20.5%
7 6514
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 36275
25.0%
0 36275
25.0%
1 36275
25.0%
8 29761
20.5%
7 6514
 
4.5%

arrival_month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4236527
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size283.5 KiB
2023-06-13T14:38:18.263829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median8
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0698944
Coefficient of variation (CV)0.41352883
Kurtosis-0.93318896
Mean7.4236527
Median Absolute Deviation (MAD)2
Skewness-0.34822885
Sum269293
Variance9.4242517
MonotonicityNot monotonic
2023-06-13T14:38:18.624814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 5317
14.7%
9 4611
12.7%
8 3813
10.5%
6 3203
8.8%
12 3021
8.3%
11 2980
8.2%
7 2920
8.0%
4 2736
7.5%
5 2598
7.2%
3 2358
6.5%
Other values (2) 2718
7.5%
ValueCountFrequency (%)
1 1014
 
2.8%
2 1704
 
4.7%
3 2358
6.5%
4 2736
7.5%
5 2598
7.2%
6 3203
8.8%
7 2920
8.0%
8 3813
10.5%
9 4611
12.7%
10 5317
14.7%
ValueCountFrequency (%)
12 3021
8.3%
11 2980
8.2%
10 5317
14.7%
9 4611
12.7%
8 3813
10.5%
7 2920
8.0%
6 3203
8.8%
5 2598
7.2%
4 2736
7.5%
3 2358
6.5%

arrival_date
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.596995
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size283.5 KiB
2023-06-13T14:38:19.008242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7404474
Coefficient of variation (CV)0.56039303
Kurtosis-1.157214
Mean15.596995
Median Absolute Deviation (MAD)8
Skewness0.028808569
Sum565781
Variance76.39542
MonotonicityNot monotonic
2023-06-13T14:38:19.400636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
13 1358
 
3.7%
17 1345
 
3.7%
2 1331
 
3.7%
4 1327
 
3.7%
19 1327
 
3.7%
16 1306
 
3.6%
20 1281
 
3.5%
15 1273
 
3.5%
6 1273
 
3.5%
18 1260
 
3.5%
Other values (21) 23194
63.9%
ValueCountFrequency (%)
1 1133
3.1%
2 1331
3.7%
3 1098
3.0%
4 1327
3.7%
5 1154
3.2%
6 1273
3.5%
7 1110
3.1%
8 1198
3.3%
9 1130
3.1%
10 1089
3.0%
ValueCountFrequency (%)
31 578
1.6%
30 1216
3.4%
29 1190
3.3%
28 1129
3.1%
27 1059
2.9%
26 1146
3.2%
25 1146
3.2%
24 1103
3.0%
23 990
2.7%
22 1023
2.8%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.5 KiB
Online
23214 
Offline
10528 
Corporate
 
2017
Complementary
 
391
Aviation
 
125

Length

Max length13
Median length6
Mean length6.5393797
Min length6

Characters and Unicode

Total characters237216
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOffline
2nd rowOnline
3rd rowOnline
4th rowOnline
5th rowOnline

Common Values

ValueCountFrequency (%)
Online 23214
64.0%
Offline 10528
29.0%
Corporate 2017
 
5.6%
Complementary 391
 
1.1%
Aviation 125
 
0.3%

Length

2023-06-13T14:38:19.831866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T14:38:20.273835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
online 23214
64.0%
offline 10528
29.0%
corporate 2017
 
5.6%
complementary 391
 
1.1%
aviation 125
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n 57472
24.2%
e 36541
15.4%
l 34133
14.4%
i 33992
14.3%
O 33742
14.2%
f 21056
 
8.9%
o 4550
 
1.9%
r 4425
 
1.9%
a 2533
 
1.1%
t 2533
 
1.1%
Other values (6) 6239
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 200941
84.7%
Uppercase Letter 36275
 
15.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 57472
28.6%
e 36541
18.2%
l 34133
17.0%
i 33992
16.9%
f 21056
 
10.5%
o 4550
 
2.3%
r 4425
 
2.2%
a 2533
 
1.3%
t 2533
 
1.3%
p 2408
 
1.2%
Other values (3) 1298
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
O 33742
93.0%
C 2408
 
6.6%
A 125
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 237216
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 57472
24.2%
e 36541
15.4%
l 34133
14.4%
i 33992
14.3%
O 33742
14.2%
f 21056
 
8.9%
o 4550
 
1.9%
r 4425
 
1.9%
a 2533
 
1.1%
t 2533
 
1.1%
Other values (6) 6239
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 237216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 57472
24.2%
e 36541
15.4%
l 34133
14.4%
i 33992
14.3%
O 33742
14.2%
f 21056
 
8.9%
o 4550
 
1.9%
r 4425
 
1.9%
a 2533
 
1.1%
t 2533
 
1.1%
Other values (6) 6239
 
2.6%

repeated_guest
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.5 KiB
0
35345 
1
 
930

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36275
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 35345
97.4%
1 930
 
2.6%

Length

2023-06-13T14:38:20.671264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T14:38:21.067622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 35345
97.4%
1 930
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 35345
97.4%
1 930
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36275
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35345
97.4%
1 930
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 36275
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 35345
97.4%
1 930
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36275
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35345
97.4%
1 930
 
2.6%

no_of_previous_cancellations
Real number (ℝ)

SKEWED  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.023349414
Minimum0
Maximum13
Zeros35937
Zeros (%)99.1%
Negative0
Negative (%)0.0%
Memory size283.5 KiB
2023-06-13T14:38:21.369348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.36833145
Coefficient of variation (CV)15.774762
Kurtosis732.73568
Mean0.023349414
Median Absolute Deviation (MAD)0
Skewness25.199876
Sum847
Variance0.13566806
MonotonicityNot monotonic
2023-06-13T14:38:21.745929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 35937
99.1%
1 198
 
0.5%
2 46
 
0.1%
3 43
 
0.1%
11 25
 
0.1%
5 11
 
< 0.1%
4 10
 
< 0.1%
13 4
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 35937
99.1%
1 198
 
0.5%
2 46
 
0.1%
3 43
 
0.1%
4 10
 
< 0.1%
5 11
 
< 0.1%
6 1
 
< 0.1%
11 25
 
0.1%
13 4
 
< 0.1%
ValueCountFrequency (%)
13 4
 
< 0.1%
11 25
 
0.1%
6 1
 
< 0.1%
5 11
 
< 0.1%
4 10
 
< 0.1%
3 43
 
0.1%
2 46
 
0.1%
1 198
 
0.5%
0 35937
99.1%

no_of_previous_bookings_not_canceled
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15341144
Minimum0
Maximum58
Zeros35463
Zeros (%)97.8%
Negative0
Negative (%)0.0%
Memory size283.5 KiB
2023-06-13T14:38:22.155317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum58
Range58
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7541707
Coefficient of variation (CV)11.434419
Kurtosis457.38009
Mean0.15341144
Median Absolute Deviation (MAD)0
Skewness19.250191
Sum5565
Variance3.0771149
MonotonicityNot monotonic
2023-06-13T14:38:22.585624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35463
97.8%
1 228
 
0.6%
2 112
 
0.3%
3 80
 
0.2%
4 65
 
0.2%
5 60
 
0.2%
6 36
 
0.1%
7 24
 
0.1%
8 23
 
0.1%
10 19
 
0.1%
Other values (49) 165
 
0.5%
ValueCountFrequency (%)
0 35463
97.8%
1 228
 
0.6%
2 112
 
0.3%
3 80
 
0.2%
4 65
 
0.2%
5 60
 
0.2%
6 36
 
0.1%
7 24
 
0.1%
8 23
 
0.1%
9 19
 
0.1%
ValueCountFrequency (%)
58 1
< 0.1%
57 1
< 0.1%
56 1
< 0.1%
55 1
< 0.1%
54 1
< 0.1%
53 1
< 0.1%
52 1
< 0.1%
51 1
< 0.1%
50 1
< 0.1%
49 1
< 0.1%

avg_price_per_room
Real number (ℝ)

Distinct3930
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.42354
Minimum0
Maximum540
Zeros545
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size283.5 KiB
2023-06-13T14:38:23.010590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile61
Q180.3
median99.45
Q3120
95-th percentile165
Maximum540
Range540
Interquartile range (IQR)39.7

Descriptive statistics

Standard deviation35.089424
Coefficient of variation (CV)0.33927889
Kurtosis3.154125
Mean103.42354
Median Absolute Deviation (MAD)20.25
Skewness0.66713287
Sum3751688.9
Variance1231.2677
MonotonicityNot monotonic
2023-06-13T14:38:23.467194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 848
 
2.3%
75 826
 
2.3%
90 703
 
1.9%
95 669
 
1.8%
115 662
 
1.8%
120 612
 
1.7%
100 604
 
1.7%
110 560
 
1.5%
0 545
 
1.5%
85 506
 
1.4%
Other values (3920) 29740
82.0%
ValueCountFrequency (%)
0 545
1.5%
0.5 1
 
< 0.1%
1 9
 
< 0.1%
1.48 1
 
< 0.1%
1.6 1
 
< 0.1%
2 6
 
< 0.1%
3 3
 
< 0.1%
4.5 1
 
< 0.1%
6 25
 
0.1%
6.5 1
 
< 0.1%
ValueCountFrequency (%)
540 1
 
< 0.1%
375.5 1
 
< 0.1%
365 1
 
< 0.1%
349.63 1
 
< 0.1%
332.57 1
 
< 0.1%
316 1
 
< 0.1%
314.1 1
 
< 0.1%
306 2
 
< 0.1%
300 5
< 0.1%
299.33 1
 
< 0.1%

no_of_special_requests
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61965541
Minimum0
Maximum5
Zeros19777
Zeros (%)54.5%
Negative0
Negative (%)0.0%
Memory size283.5 KiB
2023-06-13T14:38:23.833848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7862359
Coefficient of variation (CV)1.2688276
Kurtosis0.88143702
Mean0.61965541
Median Absolute Deviation (MAD)0
Skewness1.1450808
Sum22478
Variance0.61816689
MonotonicityNot monotonic
2023-06-13T14:38:24.188552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 19777
54.5%
1 11373
31.4%
2 4364
 
12.0%
3 675
 
1.9%
4 78
 
0.2%
5 8
 
< 0.1%
ValueCountFrequency (%)
0 19777
54.5%
1 11373
31.4%
2 4364
 
12.0%
3 675
 
1.9%
4 78
 
0.2%
5 8
 
< 0.1%
ValueCountFrequency (%)
5 8
 
< 0.1%
4 78
 
0.2%
3 675
 
1.9%
2 4364
 
12.0%
1 11373
31.4%
0 19777
54.5%

booking_status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.5 KiB
Not_Canceled
24390 
Canceled
11885 

Length

Max length12
Median length12
Mean length10.689456
Min length8

Characters and Unicode

Total characters387760
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot_Canceled
2nd rowNot_Canceled
3rd rowCanceled
4th rowCanceled
5th rowCanceled

Common Values

ValueCountFrequency (%)
Not_Canceled 24390
67.2%
Canceled 11885
32.8%

Length

2023-06-13T14:38:24.584112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T14:38:25.006714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
not_canceled 24390
67.2%
canceled 11885
32.8%

Most occurring characters

ValueCountFrequency (%)
e 72550
18.7%
C 36275
9.4%
a 36275
9.4%
n 36275
9.4%
c 36275
9.4%
l 36275
9.4%
d 36275
9.4%
N 24390
 
6.3%
o 24390
 
6.3%
t 24390
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 302705
78.1%
Uppercase Letter 60665
 
15.6%
Connector Punctuation 24390
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 72550
24.0%
a 36275
12.0%
n 36275
12.0%
c 36275
12.0%
l 36275
12.0%
d 36275
12.0%
o 24390
 
8.1%
t 24390
 
8.1%
Uppercase Letter
ValueCountFrequency (%)
C 36275
59.8%
N 24390
40.2%
Connector Punctuation
ValueCountFrequency (%)
_ 24390
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 363370
93.7%
Common 24390
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 72550
20.0%
C 36275
10.0%
a 36275
10.0%
n 36275
10.0%
c 36275
10.0%
l 36275
10.0%
d 36275
10.0%
N 24390
 
6.7%
o 24390
 
6.7%
t 24390
 
6.7%
Common
ValueCountFrequency (%)
_ 24390
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 72550
18.7%
C 36275
9.4%
a 36275
9.4%
n 36275
9.4%
c 36275
9.4%
l 36275
9.4%
d 36275
9.4%
N 24390
 
6.3%
o 24390
 
6.3%
t 24390
 
6.3%

Interactions

2023-06-13T14:38:05.002539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:38.569947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:41.878691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:44.476299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:46.872975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:49.783315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:51.878262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:55.008683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:58.260236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:01.648349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:05.337485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:38.903798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:42.211990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:44.694068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:47.205431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:49.984916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:52.092193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:55.339844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:58.592968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:01.976516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:05.677298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:39.242672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:42.563632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:44.923931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:47.547659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:50.206014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:52.318424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:55.687035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:58.942986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:02.319089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:06.019058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:39.587227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:42.932788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:45.151355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:47.887122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:50.436007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:52.674109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:56.041220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:59.297897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:02.672192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:06.346160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:39.898399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:43.154811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:45.355035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:48.109452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:50.636824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:52.989845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:56.367661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:59.626146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:02.985594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:06.677492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:40.219296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:43.374868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:45.570541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:48.305062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:50.835002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:53.318635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:56.696355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:59.959080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:03.313124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:07.013278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:40.549502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:43.595014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:45.791234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:48.960735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:51.043277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:53.657509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:57.035449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:00.299290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:03.646579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:07.353148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:40.885147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:43.816481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:46.021479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:49.172708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:51.256566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:54.011950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:57.371987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:00.637529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:03.983836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:07.689719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:41.220383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:44.046408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:46.255581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:49.386563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:51.470808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:54.354020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:57.712266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:00.980271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:04.335568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:08.027855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:41.543527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:44.261270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:46.527541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:49.584342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:51.676153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:54.680920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:37:58.045798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:01.315275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-13T14:38:04.661987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-13T14:38:25.316549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
no_of_childrenno_of_weekend_nightsno_of_week_nightslead_timearrival_montharrival_dateno_of_previous_cancellationsno_of_previous_bookings_not_canceledavg_price_per_roomno_of_special_requestsno_of_adultstype_of_meal_planrequired_car_parking_spaceroom_type_reservedarrival_yearmarket_segment_typerepeated_guestbooking_status
no_of_children1.0000.0310.019-0.026-0.0090.029-0.026-0.0340.2440.1350.1810.0370.0320.4060.0280.0620.0250.037
no_of_weekend_nights0.0311.0000.0180.099-0.0100.029-0.032-0.066-0.0260.0660.0680.0450.0290.0300.0720.1190.0670.077
no_of_week_nights0.0190.0181.0000.2450.045-0.010-0.045-0.1230.0180.0450.0750.0650.0580.0450.0300.1150.1220.106
lead_time-0.0260.0990.2451.0000.0810.000-0.101-0.191-0.021-0.0810.0980.1730.0690.0670.1470.1760.1640.438
arrival_month-0.009-0.0100.0450.0811.000-0.0430.011-0.0030.0160.0900.0950.0990.0680.0450.3950.1040.0750.172
arrival_date0.0290.029-0.0100.000-0.0431.000-0.018-0.0060.0070.0200.0360.0730.0070.0250.0860.0470.0330.035
no_of_previous_cancellations-0.026-0.032-0.045-0.1010.011-0.0181.0000.417-0.103-0.0240.0430.0150.0330.0380.0220.1060.3840.043
no_of_previous_bookings_not_canceled-0.034-0.066-0.123-0.191-0.003-0.0060.4171.000-0.1780.0010.0690.0200.0670.0340.0210.1560.5310.057
avg_price_per_room0.244-0.0260.018-0.0210.0160.007-0.103-0.1781.0000.1980.1610.1040.0640.2780.1720.3170.1610.165
no_of_special_requests0.1350.0660.045-0.0810.0900.020-0.0240.0010.1981.0000.1120.0700.0950.0750.0950.2080.0390.258
no_of_adults0.1810.0680.0750.0980.0950.0360.0430.0690.1610.1121.0000.0900.0180.3290.1020.1990.2240.096
type_of_meal_plan0.0370.0450.0650.1730.0990.0730.0150.0200.1040.0700.0901.0000.0340.1460.1960.2290.0740.087
required_car_parking_space0.0320.0290.0580.0690.0680.0070.0330.0670.0640.0950.0180.0341.0000.0450.0150.1260.1100.086
room_type_reserved0.4060.0300.0450.0670.0450.0250.0380.0340.2780.0750.3290.1460.0451.0000.1130.1650.0670.038
arrival_year0.0280.0720.0300.1470.3950.0860.0220.0210.1720.0950.1020.1960.0150.1131.0000.1890.0170.179
market_segment_type0.0620.1190.1150.1760.1040.0470.1060.1560.3170.2080.1990.2290.1260.1650.1891.0000.4690.149
repeated_guest0.0250.0670.1220.1640.0750.0330.3840.5310.1610.0390.2240.0740.1100.0670.0170.4691.0000.107
booking_status0.0370.0770.1060.4380.1720.0350.0430.0570.1650.2580.0960.0870.0860.0380.1790.1490.1071.000

Missing values

2023-06-13T14:38:08.603106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-13T14:38:10.178868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Booking_IDno_of_adultsno_of_childrenno_of_weekend_nightsno_of_week_nightstype_of_meal_planrequired_car_parking_spaceroom_type_reservedlead_timearrival_yeararrival_montharrival_datemarket_segment_typerepeated_guestno_of_previous_cancellationsno_of_previous_bookings_not_canceledavg_price_per_roomno_of_special_requestsbooking_status
0INN000012012Meal Plan 10Room_Type 12242017102Offline00065.000Not_Canceled
1INN000022023Not Selected0Room_Type 152018116Online000106.681Not_Canceled
2INN000031021Meal Plan 10Room_Type 112018228Online00060.000Canceled
3INN000042002Meal Plan 10Room_Type 12112018520Online000100.000Canceled
4INN000052011Not Selected0Room_Type 1482018411Online00094.500Canceled
5INN000062002Meal Plan 20Room_Type 13462018913Online000115.001Canceled
6INN000072013Meal Plan 10Room_Type 13420171015Online000107.551Not_Canceled
7INN000082013Meal Plan 10Room_Type 48320181226Online000105.611Not_Canceled
8INN000093004Meal Plan 10Room_Type 1121201876Offline00096.901Not_Canceled
9INN000102005Meal Plan 10Room_Type 44420181018Online000133.443Not_Canceled
Booking_IDno_of_adultsno_of_childrenno_of_weekend_nightsno_of_week_nightstype_of_meal_planrequired_car_parking_spaceroom_type_reservedlead_timearrival_yeararrival_montharrival_datemarket_segment_typerepeated_guestno_of_previous_cancellationsno_of_previous_bookings_not_canceledavg_price_per_roomno_of_special_requestsbooking_status
36265INN362662013Meal Plan 10Room_Type 1152018530Online000100.730Not_Canceled
36266INN362672022Meal Plan 10Room_Type 28201834Online00085.961Canceled
36267INN362682010Not Selected0Room_Type 1492018711Online00093.150Canceled
36268INN362691003Meal Plan 10Room_Type 11662018111Offline000110.000Canceled
36269INN362702201Meal Plan 10Room_Type 602018106Online000216.000Canceled
36270INN362713026Meal Plan 10Room_Type 485201883Online000167.801Not_Canceled
36271INN362722013Meal Plan 10Room_Type 122820181017Online00090.952Canceled
36272INN362732026Meal Plan 10Room_Type 1148201871Online00098.392Not_Canceled
36273INN362742003Not Selected0Room_Type 1632018421Online00094.500Canceled
36274INN362752012Meal Plan 10Room_Type 120720181230Offline000161.670Not_Canceled